feat: Qdrant support for the VectorDB interface

autogen/agentchat/contrib/vectordb/base.py

@@ -185,7 +185,7 @@ class VectorDBFactory:
     Factory class for creating vector databases.
     """
 
-    PREDEFINED_VECTOR_DB = ["chroma", "pgvector"]
+    PREDEFINED_VECTOR_DB = ["chroma", "pgvector", "qdrant"]
 
     @staticmethod
     def create_vector_db(db_type: str, **kwargs) -> VectorDB:
@@ -207,6 +207,10 @@ def create_vector_db(db_type: str, **kwargs) -> VectorDB:
             from .pgvectordb import PGVectorDB
 
             return PGVectorDB(**kwargs)
+        if db_type.lower() in ["qdrant", "qdrantdb"]:
+            from .qdrant import QdrantVectorDB
+
+            return QdrantVectorDB(**kwargs)
         else:
             raise ValueError(
                 f"Unsupported vector database type: {db_type}. Valid types are {VectorDBFactory.PREDEFINED_VECTOR_DB}."

autogen/agentchat/contrib/vectordb/qdrant.py

@@ -0,0 +1,316 @@
+import abc
+import logging
+import os
+from typing import Callable, List, Optional, Sequence, Tuple, Union
+
+from .base import Document, ItemID, QueryResults, VectorDB
+from .utils import get_logger
+
+try:
+    from qdrant_client import QdrantClient, models
+except ImportError:
+    raise ImportError("Please install qdrant-client: `pip install qdrant-client`")
+
+logger = get_logger(__name__)
+
+Embeddings = Union[Sequence[float], Sequence[int]]
+
+
+class EmbeddingFunction(abc.ABC):
+    @abc.abstractmethod
+    def __call__(self, inputs: List[str]) -> List[Embeddings]:
+        raise NotImplementedError
+
+
+class FastEmbedEmbeddingFunction(EmbeddingFunction):
+    """Embedding function implementation using FastEmbed - https://qdrant.github.io/fastembed."""
+
+    def __init__(
+        self,
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        batch_size: int = 256,
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        parallel: Optional[int] = None,
+        **kwargs,
+    ):
+        """Initialize fastembed.TextEmbedding.
+
+        Args:
+            model_name (str): The name of the model to use. Defaults to `"BAAI/bge-small-en-v1.5"`.
+            batch_size (int): Batch size for encoding. Higher values will use more memory, but be faster.\
+                                        Defaults to 256.
+            cache_dir (str, optional): The path to the model cache directory.\
+                                       Can also be set using the `FASTEMBED_CACHE_PATH` env variable.
+            threads (int, optional): The number of threads single onnxruntime session can use.
+            parallel (int, optional): If `>1`, data-parallel encoding will be used, recommended for large datasets.\
+                                      If `0`, use all available cores.\
+                                      If `None`, don't use data-parallel processing, use default onnxruntime threading.\
+                                      Defaults to None.
+            **kwargs: Additional options to pass to fastembed.TextEmbedding
+        Raises:
+            ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-small-en-v1.5.
+        """
+        try:
+            from fastembed import TextEmbedding
+        except ImportError as e:
+            raise ValueError(
+                "The 'fastembed' package is not installed. Please install it with `pip install fastembed`",
+            ) from e
+        self._batch_size = batch_size
+        self._parallel = parallel
+        self._model = TextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs)
+
+    def __call__(self, inputs: List[str]) -> List[Embeddings]:
+        embeddings = self._model.embed(inputs, batch_size=self._batch_size, parallel=self._parallel)
+
+        return [embedding.tolist() for embedding in embeddings]
+
+
+class QdrantVectorDB(VectorDB):
+    """
+    A vector database implementation that uses Qdrant as the backend.
+    """
+
+    def __init__(
+        self,
+        *,
+        client=None,
+        embedding_function: EmbeddingFunction = None,
+        content_payload_key: str = "_content",
+        metadata_payload_key: str = "_metadata",
+        collection_options: dict = {},
+        **kwargs,
+    ) -> None:
+        """
+        Initialize the vector database.
+
+        Args:
+            client: qdrant_client.QdrantClient | An instance of QdrantClient.
+            embedding_function: Callable | The embedding function used to generate the vector representation
+                of the documents. Defaults to FastEmbedEmbeddingFunction.
+            collection_options: dict | The options for creating the collection.
+            kwargs: dict | Additional keyword arguments.
+        """
+        self.client: QdrantClient = client
+        self.embedding_function = FastEmbedEmbeddingFunction() or embedding_function
+        self.collection_options = collection_options
+        self.content_payload_key = content_payload_key
+        self.metadata_payload_key = metadata_payload_key
+        self.type = "qdrant"
+
+    def create_collection(self, collection_name: str, overwrite: bool = False, get_or_create: bool = True) -> None:
+        """
+        Create a collection in the vector database.
+
+        Args:
+            collection_name: str | The name of the collection.
+            overwrite: bool | Whether to overwrite the collection if it exists. Default is False.
+            get_or_create: bool | Whether to get the collection if it exists. Default is True.
+
+        Returns:
+            Any | The collection object.
+        """
+        embeddings_size = len(self.embedding_function(["test"])[0])
+
+        if not self.client.collection_exists(collection_name) or overwrite:
+            self.client.create_collection(
+                collection_name,
+                vectors_config=models.VectorParams(size=embeddings_size, distance=models.Distance.COSINE),
+                **self.collection_options,
+            )
+
+    def get_collection(self, collection_name: str = None):
+        """
+        Get the collection from the vector database.
+
+        Args:
+            collection_name: str | The name of the collection.
+
+        Returns:
+            Any | The collection object.
+        """
+        if collection_name is None:
+            raise ValueError("The collection name is required.")
+
+        return self.client.get_collection(collection_name)
+
+    def delete_collection(self, collection_name: str) -> None:
+        """Delete the collection from the vector database.
+
+        Args:
+            collection_name: str | The name of the collection.
+
+        Returns:
+            Any
+        """
+        return self.client.delete_collection(collection_name)
+
+    def insert_docs(self, docs: List[Document], collection_name: str = None, upsert: bool = False) -> None:
+        """
+        Insert documents into the collection of the vector database.
+
+        Args:
+            docs: List[Document] | A list of documents. Each document is a TypedDict `Document`.
+            collection_name: str | The name of the collection. Default is None.
+            upsert: bool | Whether to update the document if it exists. Default is False.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        if not docs:
+            return
+        if any(doc.get("content") is None for doc in docs):
+            raise ValueError("The document content is required.")
+        if any(doc.get("id") is None for doc in docs):
+            raise ValueError("The document id is required.")
+
+        if not upsert and not self._validate_upsert_ids(collection_name, [doc["id"] for doc in docs]):
+            logger.log("Some IDs already exist. Skipping insert", level=logging.WARN)
+
+        self.client.upsert(collection_name, points=self._documents_to_points(docs))
+
+    def update_docs(self, docs: List[Document], collection_name: str = None) -> None:
+        if not docs:
+            return
+        if any(doc.get("id") is None for doc in docs):
+            raise ValueError("The document id is required.")
+        if any(doc.get("content") is None for doc in docs):
+            raise ValueError("The document content is required.")
+        if self._validate_update_ids(collection_name, [doc["id"] for doc in docs]):
+            return self.client.upsert(collection_name, points=self._documents_to_points(docs))
+
+        raise ValueError("Some IDs do not exist. Skipping update")
+
+    def delete_docs(self, ids: List[ItemID], collection_name: str = None, **kwargs) -> None:
+        """
+        Delete documents from the collection of the vector database.
+
+        Args:
+            ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`.
+            collection_name: str | The name of the collection. Default is None.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            None
+        """
+        self.client.delete(collection_name, ids)
+
+    def retrieve_docs(
+        self,
+        queries: List[str],
+        collection_name: str = None,
+        n_results: int = 10,
+        distance_threshold: float = 0,
+        **kwargs,
+    ) -> QueryResults:
+        """
+        Retrieve documents from the collection of the vector database based on the queries.
+
+        Args:
+            queries: List[str] | A list of queries. Each query is a string.
+            collection_name: str | The name of the collection. Default is None.
+            n_results: int | The number of relevant documents to return. Default is 10.
+            distance_threshold: float | The threshold for the distance score, only distance smaller than it will be
+                returned. Don't filter with it if < 0. Default is 0.
+            kwargs: Dict | Additional keyword arguments.
+
+        Returns:
+            QueryResults | The query results. Each query result is a list of list of tuples containing the document and
+                the distance.
+        """
+        embeddings = self.embedding_function(queries)
+        requests = [
+            models.SearchRequest(
+                vector=embedding,
+                limit=n_results,
+                score_threshold=distance_threshold,
+                with_payload=True,
+                with_vector=False,
+            )
+            for embedding in embeddings
+        ]
+
+        batch_results = self.client.search_batch(collection_name, requests)
+        return [self._scored_points_to_documents(results) for results in batch_results]
+
+    def get_docs_by_ids(
+        self, ids: List[ItemID] = None, collection_name: str = None, include=True, **kwargs
+    ) -> List[Document]:
+        """
+        Retrieve documents from the collection of the vector database based on the ids.
+
+        Args:
+            ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None.
+            collection_name: str | The name of the collection. Default is None.
+            include: List[str] | The fields to include. Default is True.
+                If None, will include ["metadatas", "documents"], ids will always be included.
+            kwargs: dict | Additional keyword arguments.
+
+        Returns:
+            List[Document] | The results.
+        """
+        results = self.client.retrieve(collection_name, ids=ids, with_payload=include, with_vectors=True)
+        return [self._point_to_document(result) for result in results]
+
+    def _point_to_document(self, point) -> Document:
+        return {
+            "id": point.id,
+            "content": point.payload.get(self.content_payload_key, ""),
+            "metadata": point.payload.get(self.metadata_payload_key, {}),
+            "embedding": point.vector,
+        }
+
+    def _points_to_documents(self, points) -> List[Document]:
+        return [self._point_to_document(point) for point in points]
+
+    def _scored_point_to_document(self, scored_point: models.ScoredPoint) -> Tuple[Document, float]:
+        return self._point_to_document(scored_point), scored_point.score
+
+    def _documents_to_points(self, documents: List[Document]):
+        contents = [document["content"] for document in documents]
+        embeddings = self.embedding_function(contents)
+        points = [
+            models.PointStruct(
+                id=documents[i]["id"],
+                vector=embeddings[i],
+                payload={
+                    self.content_payload_key: documents[i].get("content"),
+                    self.metadata_payload_key: documents[i].get("metadata"),
+                },
+            )
+            for i in range(len(documents))
+        ]
+        return points
+
+    def _scored_points_to_documents(self, scored_points: List[models.ScoredPoint]) -> List[Tuple[Document, float]]:
+        return [self._scored_point_to_document(scored_point) for scored_point in scored_points]
+
+    def _validate_update_ids(self, collection_name: str, ids: List[str]) -> bool:
+        """
+        Validates all the IDs exist in the collection
+        """
+        retrieved_ids = [
+            point.id for point in self.client.retrieve(collection_name, ids=ids, with_payload=False, with_vectors=False)
+        ]
+
+        if missing_ids := set(ids) - set(retrieved_ids):
+            logger.log(f"Missing IDs: {missing_ids}. Skipping update", level=logging.WARN)
+            return False
+
+        return True
+
+    def _validate_upsert_ids(self, collection_name: str, ids: List[str]) -> bool:
+        """
+        Validate none of the IDs exist in the collection
+        """
+        retrieved_ids = [
+            point.id for point in self.client.retrieve(collection_name, ids=ids, with_payload=False, with_vectors=False)
+        ]
+
+        if existing_ids := set(ids) & set(retrieved_ids):
+            logger.log(f"Existing IDs: {existing_ids}.", level=logging.WARN)
+            return False
+
+        return True